Data vs collateral

Summary

Focus

Collateral is used in debt contracts to mitigate the difficulties ("agency problems") that arise when the lender's knowledge of the borrower is incomplete ("asymmetric information"). Banks usually require borrowers to pledge tangible assets, such as real estate, to help offset such problems in credit assessment, or to reduce moral hazard and enforcement problems. By contrast, large technology firms ("big techs") can use massive amounts of data ("big data") to better assess firms' creditworthiness. These capabilities could help to reduce the importance of collateral in solving asymmetric information problems in credit markets.

Contribution

This paper compares how credit from a big tech firm and traditional bank lending correlate with local economic activity, house prices and firm-specific characteristics. It is based on a unique random sample of more than 2 million Chinese firms that received credit from both an important big tech firm (Ant Group) and traditional commercial banks. The paper also asks how the increased use of big data instead of collateral could affect how the provision of credit responds to collateral values. This "financial accelerator mechanism" has historically amplified the effects of financial market developments and asset prices on the real economy.

Findings

We find that big tech credit does not correlate with local business conditions and house prices when controlling for demand factors, but that it does react strongly to changes in firm-specific characteristics, such as the transaction volumes and network scores used to calculate firm credit ratings. This is particularly the case when a borrower firm conducts its business activity on the relevant e-commerce platform. By contrast, both secured and unsecured bank credit reacts significantly to local house price dynamics, which incorporate useful information on the client's creditworthiness and the business conditions in which it operates. This implies that, if big techs make greater use of machine learning and big data to provide credit, collateral would become less significant in lending, potentially weakening the financial accelerator mechanism.

Abstract

The use of massive amounts of data by large technology firms (big techs) to assess firms' creditworthiness could reduce the need for collateral in solving asymmetric information problems in credit markets. Using a unique dataset of more than 2 million Chinese firms that received credit from both an important big tech firm (Ant Group) and traditional commercial banks, this paper investigates how different forms of credit correlate with local economic activity, house prices and firm characteristics. We find that big tech credit does not correlate with local business conditions and house prices when controlling for demand factors, but reacts strongly to changes in firm characteristics, such as transaction volumes and network scores used to calculate firm credit ratings. By contrast, both secured and unsecured bank credit react significantly to local house prices, which incorporate useful information on the environment in which clients operate and on their creditworthiness. This evidence implies that a greater use of big tech credit - granted on the basis of machine learning and big data - could reduce the importance of collateral in credit markets and potentially weaken the financial accelerator mechanism.

JEL classification: D22, G31, R30

Keywords: big tech, big data, collateral, banks, asymmetric information, credit markets.